A rapid algorithm for processing digital physiologic signals: Application to skeletal muscle contractions

Quantifying mechanical output is fundamental to understanding metabolism that fuels muscle contraction and more recent attempts to understand signal transduction and gene regulation. The latter requires long-term application of exercise protocols that result in large amounts of data on muscle performance. The purpose of this study was to develop software for automated quantification of skeletal muscle contractions. An in situ mouse sciatic nerve stimulation model was used to produce contractions over a broad range of frequencies and recorded as both digital and analog signals using a PC analog to digital convener board and chart recorder, respectively. Spectral analysis of the noise components formed the basis for designing a smoothing Chebyshev filter. Algorithms implemented in Custom Software identified twitches and estimated baseline levels from the smoothed signal. The time to peak force, peak force, tension-time integral, and half-relaxation time were determined for each twitch after baseline correction. The automated results were compared to those obtained from manual measurements of the analog signal, Bland-Altman analysis of the parameters computed from digital signals compared with the corresponding measurements by manual planometry demonstrates the agreement of the digital processing algorithm with planometry over a wide range of twitch characteristics, This program may also be used to study the mechanics of other preparations from isolated muscles, human proximal limb performance, and other digital physiologic signals. Adaptation of the filter function is required to apply the analysis to mother experimental apparatus with differing noise characteristics. A full version of the program and instructions for its use are available for download at www.rad.msu.edu. Published by Elsevier Ltd.